Propofol and sevoflurane induce distinct burst suppression patterns in rats

Burst suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (bursts) and relatively low amplitude activity (suppressions). Burst suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify burst suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced burst suppression. Finally, we report the results of our new study showing clear electrophysiological differences in burst suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate burst suppression activity may differ among general anesthetics

Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded-predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award DP2-OD006454)National Institutes of Health (U.S.) (Award K08-GM094394)Burroughs Wellcome Fund (Award 1010625

Spatial variation in automated burst suppression detection in pharmacologically induced coma

Burst suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG suppression and compactly summarize the depth of coma using the burst suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in burst suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced burst suppression as medical treatment for refractory seizures. We found that although burst suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the burst suppression characteristics recorded at multiple electrodes. BSP computed from this representative burst suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.National Institutes of Health (U.S.) (Grant K23 NS090900)National Institute of Neurological Diseases and Stroke (U.S.) (Grant K23 NS090900)National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant TROI-GMI04948

Neuronal networks in burst suppression EEG patterns in newborns as revealed by source analysis [Poster]

Burst suppression (B-S): an electroencephalogram (EEG) pattern characterized by the quasiperiodic alternant phases of a high voltage activity (burst) and electrical silence (suppression), is considered as a global state of profound brain inactivation. Burst suppression can occur during different conditions such as early-onset epileptic encephalopathies, hypothermia, general anaesthesia and coma. Biophysical mechanisms underlying this broad range of inactivated brain states are poorly understood. Delta activity during the burst phases was associated with sources in the thalamus and brainstem, as well as bilateral sources in the cortical regions mainly frontal and parietal. Whereas, the suppression phases were associated with coherent sources only in the cortical regions. Results of the RPDC analyses showed an ascending informational flow from the brainstem towards the thalamus and from the thalamus to cortical regions, which was absent during the suppression phases. Our findings support the notion that “cortical deafferentation” between the cortex and subcortical structures and desynchronizations exists especially in suppression phases of burst suppression EEG

Local cortical dynamics of burst suppression in the anaesthetized brain

Burst suppression is an electroencephalogram pattern that consists of a quasi-periodic alternation between isoelectric ‘suppressions’ lasting seconds or minutes, and high-voltage ‘bursts’. It is characteristic of a profoundly inactivated brain, occurring in conditions including hypothermia, deep general anaesthesia, infant encephalopathy and coma. It is also used in neurology as an electrophysiological endpoint in pharmacologically induced coma for brain protection after traumatic injury and during status epilepticus. Classically, burst suppression has been regarded as a ‘global’ state with synchronous activity throughout cortex. This assumption has influenced the clinical use of burst suppression as a way to broadly reduce neural activity. However, the extent of spatial homogeneity has not been fully explored due to the challenges in recording from multiple cortical sites simultaneously. The neurophysiological dynamics of large-scale cortical circuits during burst suppression are therefore not well understood. To address this question, we recorded intracranial electrocorticograms from patients who entered burst suppression while receiving propofol general anaesthesia. The electrodes were broadly distributed across cortex, enabling us to examine both the dynamics of burst suppression within local cortical regions and larger-scale network interactions. We found that in contrast to previous characterizations, bursts could be substantially asynchronous across the cortex. Furthermore, the state of burst suppression itself could occur in a limited cortical region while other areas exhibited ongoing continuous activity. In addition, we found a complex temporal structure within bursts, which recapitulated the spectral dynamics of the state preceding burst suppression, and evolved throughout the course of a single burst. Our observations imply that local cortical dynamics are not homogeneous, even during significant brain inactivation. Instead, cortical and, implicitly, subcortical circuits express seemingly different sensitivities to high doses of anaesthetics that suggest a hierarchy governing how the brain enters burst suppression, and emphasize the role of local dynamics in what has previously been regarded as a global state. These findings suggest a conceptual shift in how neurologists could assess the brain function of patients undergoing burst suppression. First, analysing spatial variation in burst suppression could provide insight into the circuit dysfunction underlying a given pathology, and could improve monitoring of medically-induced coma. Second, analysing the temporal dynamics within a burst could help assess the underlying brain state. This approach could be explored as a prognostic tool for recovery from coma, and for guiding treatment of status epilepticus. Overall, these results suggest new research directions and methods that could improve patient monitoring in clinical practice.Burroughs Wellcome Fund (Career Award at the Scientific Interface)National Institutes of Health (U.S.) (Director's Pioneer Award DP10D003646)National Institutes of Health (U.S.) (Transformative 1R01GM104948

A state-space model of the burst suppression ratio

Burst suppression is an electroencephalogram pattern observed in states of severely reduced brain activity, such as general anesthesia, hypothermia and anoxic brain injuries. The burst suppression ratio (BSR), defined as the fraction of EEG spent in suppression per epoch, is the standard quantitative measure used to characterize burst suppression. We present a state space model to compute a dynamic estimate of the BSR as the instantaneous probability of suppression. We estimate the model using an approximate EM algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia. Our approach removes the need to artificially average the ratio over long epochs and allows us to make formal statistical comparisons of burst activity at different time points. Our state-space model suggests a more principled way to analyze this key EEG feature that may offer more informative assessments of its associated brain state.Massachusetts General Hospital. Dept. of Anesthesia and Critical CareNational Institutes of Health (U.S.) (Grant DP1 OD003646-01)National Institutes of Health (U.S.) (Grant R01 MH071847)National Institutes of Health (U.S.) (Grant K08 GM094394

Emergence delirium in children is not related to intraoperative burst suppression – prospective, observational electrography study

BACKGROUND: Emergence-delirium is the most frequent brain dysfunction in children recovering from general anaesthesia, though the pathophysiological background remains unclear. The presented study analysed an association between emergence delirium and intraoperative Burst Suppression activity in the electroencephalogram, a period of very deep hypnosis during general anaesthesia. METHODS: In this prospective, observational cohort study at the Charité - university hospital in Berlin / Germany children aged 0.5 to 8 years, undergoing planned surgery, were included between September 2015 and February 2017. Intraoperative bi-frontal electroencephalograms were recorded. Occurrence and duration of Burst Suppression periods were visually analysed. Emergence delirium was assessed using the Pediatric Assessment of Emergence Delirium Score. RESULTS: From 97 children being analysed within this study, 40 children developed emergence delirium, and 57 children did not. Overall 52% of the children displayed intraoperative Burst Suppression periods; however, occurrence and duration of Burst Suppression (Emergence delirium group 55% / 261 + 462 s vs. Non-emergence delirium group 49% / 318 + 531 s) did not differ significantly between both groups. CONCLUSIONS: Our data reveal no correlation between the occurrence and duration of intraoperative Burst Suppression activity and the incidence of emergence delirium. Burst Suppression occurrence is frequent; however, it does not seem to have an unfavourable impact on cerebral function at emergence from general anaesthesia in children

Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression

We provide a method for estimating brain metabolic state based on a reduced-order model of EEG burst suppression. The model, derived from previously suggested biophysical mechanisms of burst suppression, describes important electrophysiological features and provides a direct link to cerebral metabolic rate. We design and fit the estimation method from EEG recordings of burst suppression from a neurological intensive care unit and test it on real and synthetic data.National Institutes of Health (U.S.) (Grant DP1-OD003646

Identification of A Neural Mass Model of Burst Suppression

Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable (in micro or macro scale) electro-physiological recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous-discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression